This invention pertains to identifying an electronic image sensor (e.g., a CCD or CMOS sensor) from an electronic image (i.e., deciding which digital camera or other image-capture device acquired a given electronic image).
A practical and important problem in digital forensics today is to identify reliably the imaging device that acquired a particular electronic image or representation thereof. Techniques to authenticate an electronic image are especially important in court. For example, identifying the source device could establish the origin of images presented as evidence. In a prosecution for child pornography, for example, one could prove that certain imagery was obtained with a specific camera and is thus not an image generated by a computer. As electronic images and digital video replace their analog counterparts, the importance of reliable, inexpensive, and fast identification of the origin of a particular electronic image will only increase.
All prior approaches to identifying the device that acquired a particular image have significant limitations and/or limited reliability. The simplest approach is to inspect the image's electronic file itself for header information, JPEG quantization matrices, etc. However, this information can be easily modified by an attacker, or it may be lost during processing of the image. Another family of approaches either inserts an authentication watermark (Blythe, P. and Fridrich, J.: “Secure Digital Camera,” Digital Forensic Research Workshop, Baltimore, Aug. 11-13, 2004) into the image or computes an image hash (Canon Data Verification Kit DVK-E2, described at http://www.dpreview.com/news/0401/04012903canondvke2.asp) from the device. Obviously, only images produced by a very limited number of devices can be authenticated this way.
Local pixel defects have also been previously used to identify a particular device (Geradts, Z., Bijhold, J., Kieft, M., Kurosawa, K., Kuroki, K., and Saitoh, N., “Methods for Identification of Images Acquired with Digital Cameras,” Proc. of SPIE, Enabling Technologies for Law Enforcement and Security, vol. 4232, pp. 505-512, February 2001). However, there are cameras without pixel defects and cameras that remove pixel defects during post-processing of their images. Additionally, pixel defects might not be observable in every scene. This approach is not likely to be applicable to scanners either.
Another proposed prior-art method is classification by feature (Kharrazi, M., Sencar, H. T., and Memon, N.: “Blind Source Camera Identification,” Proc. ICIP '04, Singapore, Oct. 24-27, 2004). This method is less practical, since it requires a large number of images for training the classifier. Also, the reliability of this method may not rise to the standard required for legal evidence.
Kurosawa's approach (Kurosawa, K., Kuroki, K., and Saitoh, N.: “CCD Fingerprint Method—Identification of a Video Camera from Videotaped Images,” Proc of ICIP '99, Kobe, Japan, pp. 537-540, October 1999) uses the dark current of video-camera imaging sensors for the purpose of camera identification. The fact that the dark current can only be extracted from dark frames severely limits practical applications of this method. Moreover, because the dark current is a relatively weak signal, the reliability of camera identification based on dark current is limited.
Thus there is a need to identify reliably the imaging device that acquired a particular electronic image with apparatus and method that do not suffer from the drawbacks of the prior art.